Scalable Search in Computer Chess: Algorithmic Enhancements and Experiments at High Search Depths
The book presents new results of computer-chess research in the areas of selective forward pruning, the efficient application of game-theoretical knowledge, and the behavior of the search at increasing depths. It shows how to make sophisticated game-tree searchers more scalable at ever higher depths.
Ernst A. Heinz
Scalable Search in Computer Chess
Morgan Kaufmann Publishers (December, 1999)
ISBN: 3528057327
Table of contents
- Intro
- 0 Computer-Chess Primer
- Part I — Forward Pruning without Tears
- 1 Adaptive Null-Move Pruning
- 2 Extended Futility Pruning
- 3 AEL Pruning
- Part II — Integration of Perfect Knowledge
- 4 Efficient Interior-Node Recognition
- 5 Index Schemes of Endgame Databases
- 6 Knowledgeable Endgame Databases
- Part III — Search Behaviour at Increasing Depths
- 7 DarkThought Goes Deep
- 8 Modeling the “Go Deep” Behaviour
- 9 Self-Play Experiments Revisited
- Perspectives on Future Work
- Part IV — Appendices
- A How DarkThought Plays Chess
- B Tournament History of DarkThought
- C DarkThought and Test Suites
- D DarkThought at Test Games
